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Predicting Antigenicity of Influenza A Viruses Using biophysical ideas.

ABSTRACT: Antigenic variations of influenza A viruses are induced by genomic mutation in their trans-membrane protein HA1, eliciting viral escape from neutralization by antibodies generated in prior infections or vaccinations. Prediction of antigenic relationships among influenza viruses is useful for designing (or updating the existing) influenza vaccines, provides important insights into the evolutionary mechanisms underpinning viral antigenic variations, and helps to understand viral epidemiology. In this study, we present a simple and physically interpretable model that can predict antigenic relationships among influenza A viruses, based on biophysical ideas, using both genomic amino acid sequences and experimental antigenic data. We demonstrate the applicability of the model using a benchmark dataset of four subtypes of influenza A (H1N1, H3N2, H5N1, and H9N2) viruses and report on its performance profiles. Additionally, analysis of the model's parameters confirms several observations that are consistent with the findings of other previous studies, for which we provide plausible explanations.

SUBMITTER: Degoot AM

PROVIDER: S-EPMC6629677 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Predicting Antigenicity of Influenza A Viruses Using biophysical ideas.

Degoot Abdoelnaser M AM Adabor Emmanuel S ES Chirove Faraimunashe F Ndifon Wilfred W

Scientific reports 20190715 1

Antigenic variations of influenza A viruses are induced by genomic mutation in their trans-membrane protein HA1, eliciting viral escape from neutralization by antibodies generated in prior infections or vaccinations. Prediction of antigenic relationships among influenza viruses is useful for designing (or updating the existing) influenza vaccines, provides important insights into the evolutionary mechanisms underpinning viral antigenic variations, and helps to understand viral epidemiology. In t ...[more]

PMID: 31308446

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Project description:BackgroundThe evolution of influenza A viruses leads to the antigenic changes. Serological diagnosis of the antigenicity is usually labor-intensive, time-consuming and not suitable for early-stage detection. Computational prediction of the antigenic relationship between emerging and old strains of influenza viruses using viral sequences can facilitate large-scale antigenic characterization, especially for those viruses requiring high biosafety facilities, such as H5 and H7 influenza A viruses. However, most computational models require carefully designed subtype-specific features, thereby being restricted to only one subtype.MethodsIn this paper, we propose a Context-FreeEncoding Scheme (CFreeEnS) for pairs of protein sequences, which encodes a protein sequence dataset into a numeric matrix and then feeds the matrix into a downstream machine learning model. CFreeEnS is not only free from subtype-specific selected features but also able to improve the accuracy of predicting the antigenicity of influenza. Since CFreeEnS is subtype-free, it is applicable to predicting the antigenicity of diverse influenza subtypes, hopefully saving the biologists from conducting serological assays for highly pathogenic strains.ResultsThe accuracy of prediction on each subtype tested (A/H1N1, A/H3N2, A/H5N1, A/H9N2) is over 85%, and can be as high as 91.5%. This outperforms existing methods that use carefully designed subtype-specific features. Furthermore, we tested the CFreeEnS on the combined dataset of the four subtypes. The accuracy reaches 84.6%, much higher than the best performance 75.1% reported by other subtype-free models, i.e. regional band-based model and residue-based model, for predicting the antigenicity of influenza. Also, we investigate the performance of CFreeEnS when the model is trained and tested on different subtypes (i.e. transfer learning). The prediction accuracy using CFreeEnS is 84.3% when the model is trained on the A/H1N1 dataset and tested on the A/H5N1, better than the 75.2% using a regional band-based model.ConclusionsThe CFreeEnS not only improves the prediction of antigenicity on datasets with only one subtype but also outperforms existing methods when tested on a combined dataset with four subtypes of influenza viruses.

Project description:BACKGROUND: During 2009 to 2012, Thailand had encountered 4 distinctive waves of the 2009 pandemic influenza A(H1N1) (H1N1pdm) outbreaks. Considering the RNA nature of the influenza viral genome, a mutation in hemagglutinin (HA) gene which led to change in antigenicity of the strains circulating during those epidemic periods is anticipated. It is also uncertain whether the A/California/07/2009 (H1N1) (CA/07) vaccine strain still confers protective immunity against those evolved viruses, the causative agents of the later epidemic waves. METHODS: HA gene segments of 10 H1N1pdm isolates obtained during 2009 to 2012 were sequenced and phylogenetically analysed using ClustalW and MEGA5 programs. A total of 124 convalescent serum samples collected from patients naturally infected during 3 epidemic waves were employed as tools to investigate for antigenic change in HA of these 10 circulating H1N1pdm viruses by hemagglutination inhibition (HI) assay. RESULTS: A phylogenetic analysis showed that the 10 virus isolates were grouped into 4 clusters corresponding to the time of 4 consecutive outbreaks. An accumulation of amino acid substitutions in HA was observed in viruses derived from the late epidemic waves. Significantly lower antibody titers were observed when CA/07 was tested against convalescent sera collected from the 3 waves (p<0.05) compared to most of Thai isolates; and significantly lower antibody titers were also obtained when virus isolates, retrieved from the third epidemic wave were tested against convalescent sera collected during the first and second wave. These results were suggestive of change in antigenicity of the evolved viruses. Our results also showed some mutation position residing outside the previously reported antigenic site that may involve in an alteration of the viral antigenicity. CONCLUSIONS: Our study demonstrated that convalescent sera collected from individuals naturally infected with H1N1pdm virus were successfully used to reveal a statistically significant change in antibody titers against the currently evolved H1N1pdm viruses as determined by HI assay. Nevertheless, the antibody titers of individual serum against various viruses were less than 4-folded difference as compared to that against the CA/07 vaccine strain. Therefore, CA/07 is still a potent vaccine strain for those evolved H1N1pdm viruses.

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Predicting Antigenicity of Influenza A Viruses Using biophysical ideas.

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Predicting Antigenicity of Influenza A Viruses Using biophysical ideas.

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